{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d724eae",
   "metadata": {},
   "source": [
    "# Getting Started\n",
    "\n",
    "It is relatively easy to get going with a quick simulation in OpenPNM.\n",
    "In fact the following code block produces a mercury intrusion simulation in\n",
    "just a few lines."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d122c429",
   "metadata": {},
   "source": [
    "## Creating a Cubic Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1cc50017",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "══════════════════════════════════════════════════════════════════════════════\n",
      "net : <openpnm.network.Cubic at 0x7f7997db1360>\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "  #  Properties                                                   Valid Values\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "  2  pore.coords                                                   1000 / 1000\n",
      "  3  throat.conns                                                  2700 / 2700\n",
      "  4  pore.coordination_number                                      1000 / 1000\n",
      "  5  pore.max_size                                                 1000 / 1000\n",
      "  6  throat.spacing                                                2700 / 2700\n",
      "  7  pore.seed                                                     1000 / 1000\n",
      "  8  pore.diameter                                                 1000 / 1000\n",
      "  9  throat.max_size                                               2700 / 2700\n",
      " 10  throat.diameter                                               2700 / 2700\n",
      " 11  throat.cross_sectional_area                                   2700 / 2700\n",
      " 12  throat.hydraulic_size_factors                                 2700 / 2700\n",
      " 13  throat.diffusive_size_factors                                 2700 / 2700\n",
      " 14  throat.lens_volume                                            2700 / 2700\n",
      " 15  throat.length                                                 2700 / 2700\n",
      " 16  throat.total_volume                                           2700 / 2700\n",
      " 17  throat.volume                                                 2700 / 2700\n",
      " 18  pore.volume                                                   1000 / 1000\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "  #  Labels                                                 Assigned Locations\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "  2  pore.xmin                                                             100\n",
      "  3  pore.xmax                                                             100\n",
      "  4  pore.ymin                                                             100\n",
      "  5  pore.ymax                                                             100\n",
      "  6  pore.zmin                                                             100\n",
      "  7  pore.zmax                                                             100\n",
      "  8  pore.surface                                                          488\n",
      "  9  throat.surface                                                        972\n",
      " 10  pore.left                                                             100\n",
      " 11  pore.right                                                            100\n",
      " 12  pore.front                                                            100\n",
      " 13  pore.back                                                             100\n",
      " 14  pore.bottom                                                           100\n",
      " 15  pore.top                                                              100\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
     ]
    }
   ],
   "source": [
    "import openpnm as op\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "op.visualization.set_mpl_style()\n",
    "\n",
    "Nx, Ny, Nz = 10, 10, 10\n",
    "Lc = 1e-4\n",
    "pn = op.network.Cubic([Nx, Ny, Nz], spacing=Lc)\n",
    "pn.add_model_collection(op.models.collections.geometry.spheres_and_cylinders)\n",
    "pn.regenerate_models()\n",
    "print(pn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c0fe86-4466-463c-a328-106e767ee421",
   "metadata": {},
   "source": [
    "## Defining a Phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "39c7e190-2caf-4fa8-ba76-d56fae4d07b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "══════════════════════════════════════════════════════════════════════════════\n",
      "phase_01 : <openpnm.phase.Mercury at 0x7f798708fea0>\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "  #  Properties                                                   Valid Values\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "  2  pore.temperature                                              1000 / 1000\n",
      "  3  pore.pressure                                                 1000 / 1000\n",
      "  4  throat.contact_angle                                          2700 / 2700\n",
      "  5  pore.thermal_conductivity                                     1000 / 1000\n",
      "  6  pore.surface_tension                                          1000 / 1000\n",
      "  7  pore.viscosity                                                1000 / 1000\n",
      "  8  pore.density                                                  1000 / 1000\n",
      "  9  pore.molar_density                                            1000 / 1000\n",
      " 10  throat.entry_pressure                                         2700 / 2700\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "  #  Labels                                                 Assigned Locations\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "  2  pore.all                                                             1000\n",
      "  3  throat.all                                                           2700\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
     ]
    }
   ],
   "source": [
    "hg = op.phase.Mercury(network=pn)\n",
    "hg.add_model(propname='throat.entry_pressure',\n",
    "             model=op.models.physics.capillary_pressure.washburn)\n",
    "hg.regenerate_models()\n",
    "print(hg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ce76fea-8224-4eec-812e-e20986068787",
   "metadata": {},
   "source": [
    "## Performing a Drainage Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb0f3ee8-2d91-4a28-9bda-6e1abf363da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a08646409dcb47d3a2d26aef010a5c20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Performing drainage simulation:   0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mip = op.algorithms.Drainage(network=pn, phase=hg)\n",
    "mip.set_inlet_BC(pores=pn.pores(['left', 'right']))\n",
    "mip.run(pressures=np.logspace(4, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c60b5ed-f60e-4e98-9188-d2431cbe7c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 550x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 383,
       "width": 534
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = mip.pc_curve()\n",
    "fig, ax = plt.subplots(figsize=(5.5, 4))\n",
    "ax.semilogx(data.pc, data.snwp, 'k-o')\n",
    "ax.set_xlabel('capillary pressure [Pa]')\n",
    "ax.set_ylabel('mercury saturation');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f5c50c1",
   "metadata": {},
   "source": [
    "## Calculating Permeability Coefficient\n",
    "\n",
    "As another example, the permeability coefficient can be found as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aca961f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate phase and physics\n",
    "water = op.phase.Water(network=pn)\n",
    "water.add_model(propname='throat.hydraulic_conductance',\n",
    "                model=op.models.physics.hydraulic_conductance.generic_hydraulic)\n",
    "\n",
    "# Create algorithm, set boundary conditions and run simulation\n",
    "sf = op.algorithms.StokesFlow(network=pn, phase=water)\n",
    "Pin, Pout = (200_000, 101_325)\n",
    "sf.set_value_BC(pores=pn.pores('left'), values=Pin)\n",
    "sf.set_value_BC(pores=pn.pores('right'), values=Pout)\n",
    "sf.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010bcf56",
   "metadata": {},
   "source": [
    "The total flow rate into the domain through the boundary pores can be found\n",
    "using ``sf.rate(pores=pn.pores('xmin'))``. The permeability coefficient\n",
    "can be found by inserting known values into Darcy's law as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9e72cc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.44364084e-13]\n"
     ]
    }
   ],
   "source": [
    "Q = sf.rate(pores=pn.pores('left'))\n",
    "A = Ny*Nz*Lc**2\n",
    "L = Nx*Lc\n",
    "mu = water['pore.viscosity'].mean()\n",
    "K = Q*mu*L/(A*(Pin-Pout))\n",
    "print(K)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4cdab94",
   "metadata": {},
   "source": [
    "## Adjusting Pore Size Distribution\n",
    "\n",
    "It's also worth explaining how to adjust the pore size distribution of the network, so that the capillary curve and permeability coefficient can be changed to match known values. The ``geo`` object controls the geometric properties, and it possess models to calculate values on demand. Let's change the pore size distribution to a Weibull distribution, but first let's store the existing values in a dummy variable so we can compare later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ab23c414",
   "metadata": {},
   "outputs": [],
   "source": [
    "import openpnm.models.geometry as gmods\n",
    "\n",
    "pn['pore.old_diameter'] = pn.pop('pore.diameter')\n",
    "pn.add_model(propname='pore.diameter',\n",
    "             model=gmods.pore_size.weibull,\n",
    "             shape=0.5, loc=0, scale=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252bdeb6",
   "metadata": {},
   "source": [
    "Now you can use `matplotlib.` to get a quick glance at the histograms of the two distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1aae9dfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 434,
       "width": 584
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4.5))\n",
    "ax.hist(pn['pore.diameter'], edgecolor='k', label='new diameter')\n",
    "ax.hist(pn['pore.old_diameter'], edgecolor='k', label='old_diameter', bins=20)\n",
    "ax.set_xlabel('diameter [m]')\n",
    "ax.set_ylabel('frequency')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0251bc1d",
   "metadata": {},
   "source": [
    "More complex tasks are explained in the [examples](../_examples/index) page."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
